The exponential growth of content available on the Internet, as well as the more recent rise of social media sites on the Internet (such as FLICKR® or YOUTUBE®), has facilitated the growth of a new user communities that enable users across the world to share and view images, videos and ideas in new and accessible ways. The ease of sharing media with other users, however, incurs the downside of easing the spread of offensive and unsafe content from malicious users to the world. Additionally, the global nature of the Internet has seen this amount of unsafe content increase at an equally exponential rate, resulting in a massive amount of unsafe content available on the Internet.
A major concern involves how to protect users who do not wish to view unsafe content while maintaining the freedom and artistic expression nurtured by the nature of social media websites. The current state of the art relies on mechanisms to manually identify unsafe images during the upload phase of supplying user content. This mechanism may be as simple as human editors reviewing uploaded content as such content is received or a subset of uploaded content. This approach, however, suffers from the consumption of time required by human editors.
While automatic solutions reduce the amount of unsafe content uploaded to social media sites, it is unfeasible that for purely mechanical solutions to detect a majority, if not all, unsafe content items. Conversely, however, the number of content items uploaded to a social media site may be on the order of millions of content items per day, an unfeasible amount of data for a team of human editors to review. Thus, there is a need in the art for systems, methods and computer program products that combine the speed of an automated unsafe content filter with the precision of the human factor of reviewing potentially unsafe content.